Zoom-In to Sort AI-Generated Images Out

The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising critical concerns for digital integrity. Vision-language models (VLMs) offer interpretability through explanations but often fail to detect subtle artifacts in high-quality synthetic images. We propose ZoomIn, a two-stage forensic framework that improves both accuracy and interpretability. Mimicking human visual inspection, ZoomIn first scans an image to locate suspicious regions and then performs a focused analysis on these zoomed-in areas to deliver a grounded verdict. To support training, we introduce MagniFake, a dataset of 20,000 real and high-quality synthetic images annotated with bounding boxes and forensic explanations, generated through an automated VLM-based pipeline. Our approach achieves 96.39% accuracy with strong generalization across external datasets, and providing human-understandable explanations grounded in visual evidence. The rapid advancement of image generation models (Wang et al., 2025b; Li et al., 2025; Chadebec et al., 2025) has enabled the creation of AI-generated images with unprecedented photorealism, increasingly blurring the boundary between authentic and synthetic content. There is a critical need for platforms to deploy accurate and effective detection methods. However, the current landscape of detection methods is dominated by classification-based approaches. While often effective on specific datasets, these methods typically operate as "black-or gray-boxes", offering little insight into their decision-making process. This lack of explainability is coupled with poor generalizability, as models trained to detect artifacts from one generative architecture often fail when confronted with novel, unseen ones.